Soil moisture retrieval, Short Term Change Detection, Sentinel-1 & -2
LAI green, LAI brown, Sentinel-2, Gaussian processes regression
ATBD, Sentinel-1, Sentinel-2, multi-temporal, JECAM
Crop map clasiffication, Sentinel-1 & -2.
This detailed crops and natural land map, up to 145 classes, is presented as the result of applying a classification algorithm over the test site of Castile and León. This land cover map has been initially derived from Sentinel-2 satellite data and ancillary data. In the next year, we plan to incorporate Sentinel-1 radar data in order to improve the final map.
ITACyL, Agro-Technological Institute of Castilla y León (ITACyL), Vicente del Blanco Medina , Vanessa Paredes Gómez and David A. Nafría Garcíainfo_outline
LAI green and LAI brown maps obtained from applying independent Gaussian Processes Regression retrieval models to Sentinel-2 (S2) images (Tile size).
UVEG, Eatidal Amin Jochem Verrelst Juan Pablo Rivera Jesus Delegido Nieves Pasqualotto Antonio Ruiz Verdúinfo_outline
The SENSAGRI Sentinel-2 LAI green and brown product: from algorithm development towards operational mapping. IGARSS 2018 (submitted).
The SENSAGRI High Resolution Surface Soil Moisture (SSM) product is derived from time series of co-registered Sentinel-1 IW (S-1) and Sentinel-2 (S-2) observations, at 40m pixel size (~100m resolution), by using the SMOSAR code that implements a short term change detection (STCD) retrieval algorithm . In order to improve the radiometric resolution of the obtained SSM maps an averaging is required. This is implemented with an adaptive strategy that performs averages at field scale over agricultural areas, where parcel borders are available, and at 0.5km scale over the remaining areas. The final product is delivered at 50m pixel size.
CNR-ISSIA, Francesco Mattia, http://www.issia.cnr.it/wp/topics-earth-observation/
The seasonal Crop Maps Product consists of a set of maps that describe crop areas along the agricultural year. Those maps are calculated by combining information from Sentinel-1 and -2 times series. The main objective is to produce robust, accurate and frequently updated seasonal crop maps that would be available early in the year. From an earliest times in the year, the product is developed in order to forecast the main crops that will be cultivated on a specific area at the end of the season. The proposed methodology relies on a supervised classification system which exploits the different multi-temporal data of the Sentinel missions: the weather independent acquisitions of Sentinel-1 and the high spatial-spectral resolution of Sentinel-2. Two seasonal products are expected to be delivered: (1) a binary cropland mask and (2) a crop type map. For both products, the cropland and crop type legends proposed by JECAM (Joint Experiment Crop Assessment and Monitoring) are used. Hence, cropland areas contain the regions where at least one crop has been planted along the year (not including permanent grasslands, woody vegetation and either permanent crop covers). This document provides a detailed description and justification of the proposed algorithm.
CESBIO, Ludovic ARNAUD, VALERO Silviainfo_outline
Product documentation Binary cropland mask and crop type product are based on the ESA Sen2Agri project , where the Cesbio team was in charge of the algorithm design and the benchmarking phases. The definition of annual cropland and the used legend relies on JECAM protocol since these definitions are relevant and compatible for, in situ and satellite remote sensing observations. Binary Crop Mask The binary crop mask consist in a binary map distinguishing between ""annual cropland"" and ""other than annual cropland"" areas [6, 3] .The annual cropland is defined as a piece of land with a minimum are of 0.25ha, actually sowed/planted and harvestable at least once within the year following the sowing date (not including permanent grasslands, woody vegetation and either permanent crop covers). This binary map will be produced along the agricultural season, to serve as a mask for monitoring crop growing conditions. Its accuracy is expected to increase along the agricultural year when additional images are integrated into the processing chain. Crop Type map The crop type map is a map of the main crop types (or crop groups) for a given region at 10 meter spatial resolution [6, 2] . The main crop types are defined as (i) those covering a minimum area of 5% of the annual cropland area and (ii) whose cumulated area represent more than 75% of the annual cropland area. The rationale for these 5% and 75% thresholds is to avoid crop types that are weakly represented and therefore, too costly for field campaign.
In the emerging Copernicus Earth monitoring era, Europe provides Earth Observation (EO) data from Sentinel-1 (S1) and Sentinel-2 (S2) on a free and open data policy basis. In response of the EO Work programme "EO-3-2016: Evolution of Copernicus Services", Sentinels Synergy for Agriculture (SENSAGRI) aims to exploit the unprecedented capacity of S1 and S2 to develop an innovative portfolio of prototypes agricultural monitoring services. When used alone either optical or radar sensors allow the mapping of crop types. However more robust, accurate, frequently updated and comprehensive crop maps are expected from the seldom exploited synergy of both types of measurements. The same holds when dealing with crop status, health and stresses. Experimental studies have demonstrated that fusion of optical and radar data opens up prospects for enhanced monitoring capabilities. To know more, please visite the website http://sensagri.eu
The SENSAGRI project is on still ongoing project, the present webGIS displays SENSAGRI products at their current state of progress only for communication purpose. In this way, SENSAGRI consortium has no liability over the inappropriate use of its website content.
The Living Lab approach supports the co-creation of remote sensing based services and consists of the following steps : we drive the experimentation of a first demonstrator, that is assessed by users and efficiently co-designed with designers and community of practices and finally update the first demonstrator to a more stable delivering service. The more this cycle is repeated, the more context-aware is the prototype and the more the business and organizational models are concretized. In this mission, we :
Identify potential end-user and transfer the knowledge of potential application of remote sensing technology.
Through an open-design process, ideate hypothesis of services from deep understanding of user needs. Then assess and select ideas depending on their service experience maturity (SRL) and on the space technology maturity (TRL). Develop user supported projects and document the open design process. Capitalize and document the process to transfer to other European pilot partners ( Italy, Poland, Spain). French User Community SENSAGRI benefits from recent projects of E2L where geo web services were already prototyped in partnership with 1) Agrod'OC, an independent union of regional CETA (studies center of agricultural techniques) that stimulates the agricultural community and 2) the CACG, a regional development society, commit to implement the river basins water policy and management.
To know more, visite this link : https://e2l-coop.eu/en/projects/h2020-sensagri